18,024 research outputs found
Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis
We introduce a data-driven approach to complete partial 3D shapes through a
combination of volumetric deep neural networks and 3D shape synthesis. From a
partially-scanned input shape, our method first infers a low-resolution -- but
complete -- output. To this end, we introduce a 3D-Encoder-Predictor Network
(3D-EPN) which is composed of 3D convolutional layers. The network is trained
to predict and fill in missing data, and operates on an implicit surface
representation that encodes both known and unknown space. This allows us to
predict global structure in unknown areas at high accuracy. We then correlate
these intermediary results with 3D geometry from a shape database at test time.
In a final pass, we propose a patch-based 3D shape synthesis method that
imposes the 3D geometry from these retrieved shapes as constraints on the
coarsely-completed mesh. This synthesis process enables us to reconstruct
fine-scale detail and generate high-resolution output while respecting the
global mesh structure obtained by the 3D-EPN. Although our 3D-EPN outperforms
state-of-the-art completion method, the main contribution in our work lies in
the combination of a data-driven shape predictor and analytic 3D shape
synthesis. In our results, we show extensive evaluations on a newly-introduced
shape completion benchmark for both real-world and synthetic data
Pushing the Limits of 3D Color Printing: Error Diffusion with Translucent Materials
Accurate color reproduction is important in many applications of 3D printing,
from design prototypes to 3D color copies or portraits. Although full color is
available via other technologies, multi-jet printers have greater potential for
graphical 3D printing, in terms of reproducing complex appearance properties.
However, to date these printers cannot produce full color, and doing so poses
substantial technical challenges, from the shear amount of data to the
translucency of the available color materials. In this paper, we propose an
error diffusion halftoning approach to achieve full color with multi-jet
printers, which operates on multiple isosurfaces or layers within the object.
We propose a novel traversal algorithm for voxel surfaces, which allows the
transfer of existing error diffusion algorithms from 2D printing. The resulting
prints faithfully reproduce colors, color gradients and fine-scale details.Comment: 15 pages, 14 figures; includes supplemental figure
A survey of real-time crowd rendering
In this survey we review, classify and compare existing approaches for real-time crowd rendering. We first overview character animation techniques, as they are highly tied to crowd rendering performance, and then we analyze the state of the art in crowd rendering. We discuss different representations for level-of-detail (LoD) rendering of animated characters, including polygon-based, point-based, and image-based techniques, and review different criteria for runtime LoD selection. Besides LoD approaches, we review classic acceleration schemes, such as frustum culling and occlusion culling, and describe how they can be adapted to handle crowds of animated characters. We also discuss specific acceleration techniques for crowd rendering, such as primitive pseudo-instancing, palette skinning, and dynamic key-pose caching, which benefit from current graphics hardware. We also address other factors affecting performance and realism of crowds such as lighting, shadowing, clothing and variability. Finally we provide an exhaustive comparison of the most relevant approaches in the field.Peer ReviewedPostprint (author's final draft
CUDA based Level Set Method for 3D Reconstruction of Fishes from Large Acoustic Data
Acoustic images present views of underwater dynamics, even in high depths. With multi-beam echo sounders (SONARs), it
is possible to capture series of 2D high resolution acoustic images. 3D reconstruction of the water column and subsequent
estimation of fish abundance and fish species identification is highly desirable for planning sustainable fisheries. Main hurdles
in analysing acoustic images are the presence of speckle noise and the vast amount of acoustic data. This paper presents a level
set formulation for simultaneous fish reconstruction and noise suppression from raw acoustic images. Despite the presence of
speckle noise blobs, actual fish intensity values can be distinguished by extremely high values, varying exponentially from the
background. Edge detection generally gives excessive false edges that are not reliable. Our approach to reconstruction is based
on level set evolution using Mumford-Shah segmentation functional that does not depend on edges in an image. We use the
implicit function in conjunction with the image to robustly estimate a threshold for suppressing noise in the image by solving
a second differential equation. We provide details of our estimation of suppressing threshold and show its convergence as the
evolution proceeds. We also present a GPU based streaming computation of the method using NVIDIA’s CUDA framework to
handle large volume data-sets. Our implementation is optimised for memory usage to handle large volumes
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